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Multi-Criteria Selection of Electric Delivery Vehicles Using Fuzzy–Rough Methods

Author

Listed:
  • Ning Wang

    (School of Management, Shandong Technology and Business University, No. 191 Binhai Zhong Road, Laishan District, Yantai 264005, China)

  • Yong Xu

    (School of Management, Shandong Technology and Business University, No. 191 Binhai Zhong Road, Laishan District, Yantai 264005, China)

  • Adis Puška

    (Department of Public Safety, Government of Brčko District of Bosnia and Herzegovina, Bulevara Mira 1, 76100 Brčko, Bosnia and Herzegovina)

  • Željko Stević

    (Faculty of Transport and Traffic Engineering, University of East Sarajevo, Vojvode Mišića 52, 74000 Doboj, Bosnia and Herzegovina)

  • Adel Fahad Alrasheedi

    (Department of Statistics and Operations Research, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia)

Abstract

Urban logistics implementation causes environmental pollution; therefore, it is necessary to consider the impact on the environment when carrying out such logistics. Electric vehicles are alternative vehicles that reduce the impact on the environment. For this reason, this study investigated which electric vehicle has the best indicators for urban logistics. An innovative approach when selecting such vehicles is the application of a fuzzy–rough method based on expert decision making, whereby the decision-making process is adapted to the decision makers. In this case, two methods of multi-criteria decision making (MCDM) were used: SWARA (stepwise weight assessment ratio analysis) and MARCOS (measurement alternatives and ranking according to compromise solution). By applying the fuzzy–rough approach, uncertainty is included when making a decision, and it is possible to use linguistic values. The results obtained by the fuzzy–rough SWARA method showed that the range and price of electric vehicles have the greatest influence on the selection of an electric delivery vehicle. The results of applying the fuzzy–rough MARCOS method indicated that the Kangoo E-Tech Electric vehicle has the best characteristics according to experts’ estimates. These results were confirmed by validation and the application of sensitivity analysis. In urban logistics, the selection of an electric delivery vehicle helps to reduce the impact on the environment. By applying the fuzzy–rough approach, the decision-making problem is adjusted to the preferences of the decision makers who play a major role in purchasing a vehicle.

Suggested Citation

  • Ning Wang & Yong Xu & Adis Puška & Željko Stević & Adel Fahad Alrasheedi, 2023. "Multi-Criteria Selection of Electric Delivery Vehicles Using Fuzzy–Rough Methods," Sustainability, MDPI, vol. 15(21), pages 1-25, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:21:p:15541-:d:1272615
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    References listed on IDEAS

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    1. Li, Zhenhe & Khajepour, Amir & Song, Jinchun, 2019. "A comprehensive review of the key technologies for pure electric vehicles," Energy, Elsevier, vol. 182(C), pages 824-839.
    2. Nils Boysen & Stefan Fedtke & Stefan Schwerdfeger, 2021. "Last-mile delivery concepts: a survey from an operational research perspective," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(1), pages 1-58, March.
    3. Xuemei Chen & Bin Zhou & Anđelka Štilić & Željko Stević & Adis Puška, 2023. "A Fuzzy–Rough MCDM Approach for Selecting Green Suppliers in the Furniture Manufacturing Industry: A Case Study of Eco-Friendly Material Production," Sustainability, MDPI, vol. 15(13), pages 1-21, July.
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    Cited by:

    1. Gurusamy, Azhaganathan & Ashok, Bragadeshwaran & Alsaif, Faisal & Suresh, Vishnu, 2024. "Multifaceted multi-criteria decision making framework to prioritise the electric two-wheelers based on standard and regional driving cycles," Energy, Elsevier, vol. 305(C).
    2. Bošković, Sara & Švadlenka, Libor & Jovčić, Stefan & Simic, Vladimir & Dobrodolac, Momčilo & Elomiya, Akram, 2024. "Sustainable propulsion technology selection in penultimate mile delivery using the FullEX-AROMAN method," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).

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